Nowadays, the heterogeneous nature of mobile terminals, electronic devices, communications and multimedia applications renders multimedia transcoding inevitable. For example, in the emerging Multimedia Messaging Service (MMS), server-end adaptation is necessary to ensure interoperability when the destination mobile terminal cannot handle the received media under its current format. Image-related interoperability problems mainly originate from excessive resolution or file size. Accordingly, image transcoding operations commonly involve image scaling and file size reduction.
Although reducing the resolution of an image is a well-known and deterministic problem, reducing efficiently the compressed file size of an image in order to meet a given target remains a challenge. For example, in the lossy JPEG (Joint Photographic Experts Group) format, the user typically controls a quality factor (QF), which affects the quantization process and therefore the compressed file size. Indeed, a higher QF leads to a better image quality and a larger file size. However, a precise relationship between the QF and the compressed file size still lacks, since other image properties must also be taken into consideration when establishing the relationship between the QF and the compressed file size.
A simple transcoding approach for file size reduction of an image may consist of decoding the image and then iteratively re-encoding the image with a different QF value until the given target size is met, within acceptable tolerance. Although functional, this approach is highly inefficient in terms of computations and is not acceptable for high volume image transcoding servers.
Several studies have investigated the relationship between quantization and file size, or the bitrate. Although these studies provide interesting results, they are difficult to implement in the proposed context of predicting a JPEG image file size subject to transformation by scaling and a change of QF value, because many assumptions do not hold. For example, most of the studies start from an original, artefact-free image. Also some of these studies were made in the context of MPEG video coding, which uses a simpler quantization scheme than JPEG. More importantly, these studies ignore scaling of the image as a bona fide adaptation strategy. The impact of these differences with the proposed context will be discussed herein below.
Furthermore, interesting methods have been proposed to address the specific problem of JPEG size adaptation (see for example U.S. Pat. No. 6,233,359 B1 granted to Ratnakar et al on May 15, 2001 and the article “Efficient transform-domain size and resolution reduction of images”, by J. Ridge, Signal Processing: Image communication, 18(8):621-639, September 2003”). As discussed in the article of J. Ridge, the method of U.S. Pat. No. 6,233,359 B1 addresses complexity more than user experience and, also, this method is prone to undershooting file sizes, which represent two major shortcomings. Even though the method of J. Ridge provides much better results than the method disclosed in U.S. Pat. No. 6,233,359 B1, these two methods still have major drawbacks or limitations, in particular their non-treatment of scaling as a file size reduction strategy, so that there is a need to further investigate and improve the existing methods.
For example, the existing algorithms first require that some image statistics be gathered. By so doing, not only the complexity of the process is increased but also some level of re-engineering of the image compression tools is required, so that the JPEG encoder/decoder software has to become a specialized transcoder.
Secondly, those algorithms consider the resolution of the image as fixed, or independently altered in a previous stage, and focus solely on file size reduction. However, the study of the impact of changes in both quality-controlling parameters and scaling appears to be a necessity. Indeed, this will be useful so as to select the best combination of scaling and QF values which will meet terminal constraints. In particular, it is often better to have a lower resolution, high quality image than a high-resolution image with poor quality.